CN104049221B - Supply voltage method for diagnosing faults based on sliding window and statistical information - Google Patents
Supply voltage method for diagnosing faults based on sliding window and statistical information Download PDFInfo
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Abstract
Supply voltage method for diagnosing faults based on sliding window and statistical information, relates to supply voltage fault diagnosis technology.It is for solving the degree of accuracy of existing power supply voltage failure diagnostic method and the problem that diagnosis efficiency is low.First the statistical information of supply voltage duty is calculated, determine the average statistical { m that all working state is corresponding1,m2,m3,…,msAnd the poor { d of SS1,d2,d3,…,ds, continuous acquisition real satellite supply voltage data, form sliding window data V and calculate its average statistical mvWith standard deviation dv, calculate dvWith diMinimum range Rj, when | mv‑‑mj|<RjTime, then the duty of current power voltage is state j.The method diagnosis accuracy reaches 99.6%, and diagnosis efficiency improves more than 200%.The present invention is applicable to the fault diagnosis of satellite power supply and other spacecraft.
Description
Technical field
The present invention relates to supply voltage fault diagnosis technology.
Background technology
Power supply is the important component part of satellite, its duty stabilize to satellite general safety, stable operation provide important
Ensure, therefore, its duty is monitored, identifies, and it is significant to carry out fault diagnosis on this basis.
The magnitude of voltage of satellite voltage also dynamically changes with load change, and also there is accidental exceptional value.As shown in Figure 1
It is the situation of change of certain bus voltage value, in addition to duty frequently changes, there is also accidental peak voltage value.At present,
Due to the particularity of mains voltage variations, classical method for diagnosing faults is difficult to the fault diagnosis of high accuracy, for electricity
It is the lowest that the fault diagnosis of source state carries out manual identified, the degree of accuracy and diagnosis efficiency mainly by experienced expert.
Summary of the invention
The degree of accuracy that the invention aims to solve existing supply voltage method for diagnosing faults is low low with diagnosis efficiency
Problem, it is provided that a kind of supply voltage method for diagnosing faults based on sliding window and statistical information.
Supply voltage method for diagnosing faults based on sliding window and statistical information of the present invention comprises the following steps:
Step one, the statistical information of supply voltage duty calculate
The supply voltage historical data collected is analyzed, determines number s and the data set of correspondence of its duty
Close, and the data acquisition system of each duty is carried out statistical computation, obtain the average statistical that all working state is corresponding
M={m1,m2,m3,…,msAnd SS difference D={d1,d2,d3,…,ds}；Wherein, m1To msRepresent the 1st respectively
To the average statistical of s duty, d1To dsRepresent that the 1st is poor to the SS of s duty respectively, s
For the integer more than 1, described duty includes normal operating conditions and s-1 different types of malfunction；
Step 2, data acquisition based on sliding window
Gather real satellite supply voltage data vk, intercept with current power voltage data vkFor the one piece of data V of starting point,
V={vk,vk-1,vk-2,vk-3,…,vk-w+1, as sliding window data, wherein, k > 0, w > 0, k-w+1 > 0, w are for sliding
The width of dynamic window；
Step 3, the statistical computation of sliding window data
To sliding window data V={vk,vk-1,vk2,vk-3,…,vk-w+1Carry out statistical computation, obtain the average statistical of its correspondence
mvWith standard deviation dv；
Step 4, current power voltage ratings identification and fault diagnosis
Standard deviation d of sliding window data is calculated according to formula (1)vStatistics mark with each duty that step one obtains
Accurate poor { diDistance { Ri, i is integer, and 1≤i≤s, and therefrom finds out the distance value R of minimumj, j ∈ 1,2 ..., s},
I.e. Rj=min{Ri,
Ri=dv-di, (1)
When | mv--mj|<RjTime, the duty of current power voltage is state j, and then obtains the work of current power voltage
State is normal operating conditions or malfunction and the type of malfunction, otherwise, the duty of current power voltage
For the malfunction of UNKNOWN TYPE, and the malfunction of this UNKNOWN TYPE is stored.
The present invention uses sliding window, scans and identify the duty of supply voltage the most in real time, it is possible to realize high efficiency,
The fault diagnosis of high accuracy, the calculating time complexity of the method is O (n).By the survey to real satellite supply voltage
Examination, the fault diagnosis degree of accuracy of said method reaches 99.6%, compared with Artificial Diagnosis, diagnosis efficiency improve 200% with
On, provide strong technical support for follow-up fault reasoning and location.
Accompanying drawing explanation
Fig. 1 is the situation of change of certain power rail voltage value in background technology；
Fig. 2 is the flow chart of supply voltage method for diagnosing faults based on sliding window and statistical information of the present invention.
Detailed description of the invention
Detailed description of the invention one: combine Fig. 2 and present embodiment is described, described in present embodiment based on sliding window and system
The supply voltage method for diagnosing faults of meter information comprises the following steps:
Step one, the statistical information of supply voltage duty calculate
The supply voltage historical data collected is analyzed, determines number s and the data set of correspondence of its duty
Close, and the data acquisition system of each duty is carried out statistical computation, obtain the average statistical that all working state is corresponding
M={m1,m2,m3,…,msAnd SS difference D={d1,d2,d3,…,ds}；Wherein, m1、m2、m3... and
msRepresent the average statistical of the 1st, 2 ... and s duty, d respectively1、d2……d3... and dsRespectively
Representing that the SS of the 1st, 2 ... and s duty is poor, s is the integer more than 1, described work
State includes normal operating conditions and s-1 different types of malfunction；
Step 2, data acquisition based on sliding window
Gather real satellite supply voltage data, intercept with current power voltage data vkOne piece of data V, V={v for starting pointk,
vk-1,vk2,vk-3,…,vk-w+1, as sliding window data, wherein, k > 0, w > 0, k-w+1 > 0, w are sliding window
Width；
Step 3, the statistical computation of sliding window data
To sliding window data V={vk,vk-1,vk2,vk-3,…,vk-w+1Carry out statistical computation, obtain the average statistical of its correspondence
mvWith standard deviation dv；
Step 4, current power voltage ratings identification and fault diagnosis
Standard deviation d of sliding window data is calculated according to formula (1)vStatistics mark with each duty that step one obtains
Accurate poor { diDistance { Ri, i is integer, and 1≤i≤s, and therefrom finds out the distance value R of minimumj, j ∈ 1,2 ..., s},
I.e. Rj=min{Ri,
Ri=dv-di, (1)
When | mv--mj|<RjTime, the duty of current power voltage is state j, and then obtains the work of current power voltage
State is normal operating conditions or malfunction and the type of malfunction, otherwise, the duty of current power voltage
For the malfunction of UNKNOWN TYPE, and the malfunction of this UNKNOWN TYPE is stored.
The type of common malfunction has: overcharging high pressure, overload low pressure, power supply low pressure etc., to normal operating conditions with every
The malfunction of individual type is added up, and obtains M and D.
In above-mentioned steps two, to real satellite supply voltage data viWhen being operated state recognition and fault diagnosis, use
Sliding window structure, window width is w, if processing data width is less than w, is then as the criterion with the developed width of data.Step
In four, due to total s-1 different types of malfunction type, i.e. malfunction 1 to malfunction s-1, therefore,
When the duty determining current power voltage is state j, i.e. can determine that the duty of current power voltage is normal work
Make state or malfunction, and the type of malfunction.
Supply voltage method for diagnosing faults based on sliding window and statistical information described in present embodiment uses sliding window,
The online duty scanning and identifying supply voltage in real time, it is possible to realize the fault diagnosis of high efficiency, high accuracy, should
The calculating time complexity of method is O (n).By the test to real satellite supply voltage, the fault diagnosis of said method
The degree of accuracy reaches 99.6%, and compared with Artificial Diagnosis, diagnosis efficiency improves more than 200%, for follow-up fault reasoning and
Location provides strong technical support.The method is applicable to the fault diagnosis in satellite power supply, and other satellite monitoring number
According to fault diagnosis field, can be extended in the diagnosis application of other spacecraft simultaneously.
Claims (1)
1. supply voltage method for diagnosing faults based on sliding window and statistical information, it is characterised in that: the method includes following
Step:
Step one, the statistical information of supply voltage duty calculate
The supply voltage historical data collected is analyzed, determines number s and the data set of correspondence of its duty
Close, and the data acquisition system of each duty is carried out statistical computation, obtain the average statistical that all working state is corresponding
M={m1,m2,m3,…,msAnd SS difference D={d1,d2,d3,…,ds}；Wherein, m1To msRespectively represent the 1st to
The average statistical of s duty, d1To dsRepresenting that the 1st is poor to the SS of s duty respectively, s is big
In the integer of 1, described duty includes normal operating conditions and s-1 different types of malfunction；
Step 2, data acquisition based on sliding window
Gather real satellite supply voltage data vk, intercept with current power voltage data vkOne piece of data V, V={v for starting pointk,
vk-1,vk-2,vk-3,…,vk-w+1, as sliding window data, wherein, k > 0, w > 0, k-w+1 > 0, w are sliding window
Width；
Step 3, the statistical computation of sliding window data
To sliding window data V={vk,vk-1,vk2,vk-3,…,vk-w+1Carry out statistical computation, obtain the average statistical m of its correspondencev
With standard deviation dv；
Step 4, current power voltage ratings identification and fault diagnosis
Standard deviation d of sliding window data is calculated according to formula (1)vSS with each duty that step one obtains
Difference { diDistance { Ri, i is integer, and 1≤i≤s, and therefrom finds out the distance value R of minimumj, j ∈ 1,2 ..., s},
I.e. Rj=min{Ri,
Ri=dv-di, (1)
When | mv-mj|<RjTime, the duty of current power voltage is state j, and then obtains the work shape of current power voltage
State is normal operating conditions or malfunction and the type of malfunction, and otherwise, the duty of current power voltage is
The malfunction of UNKNOWN TYPE, and the malfunction of this UNKNOWN TYPE is stored.
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| CN104749532B (en) * | 2015-03-20 | 2018-01-09 | 南京航空航天大学 | A kind of spacecraft power supply system failure detection method and device |
| CN108693469A (en) * | 2018-06-12 | 2018-10-23 | 广东电网有限责任公司 | The method for diagnosing faults and device of GIS device |
| CN112857806B (en) * | 2021-03-13 | 2022-05-31 | 宁波大学科学技术学院 | Bearing fault detection method based on moving window time domain feature extraction |
| CN114942387B (en) * | 2022-07-20 | 2022-10-25 | 湖北工业大学 | Real data-based power battery fault online detection method and system |
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| EP2330728A1 (en) * | 2008-09-22 | 2011-06-08 | Fujitsu Limited | Power control circuit, power supply unit, power supply system, and power controller control method |
| CN101713818A (en) * | 2009-11-13 | 2010-05-26 | 航天东方红卫星有限公司 | Working condition automatic interpreting system of satellite power source subsystem |
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